Diabetes distress (DD) refers to the unique emotional burdens, worries and concerns that are part of the spectrum of patient experience when managing a severe, demanding chronic disease like diabetes. High DD is very common and persistent over time among those with diabetes, and it has been significantly associated with poor glycemic control, poor self-care, low diabetes self-efficacy, and poor quality of life, even after controlling for clinical depression. The vast majority of research in DD has focused on adults with type 2 diabetes, a metabolic disease based on genetic predisposition, obesity, sedentary lifestyle and insulin resistance. Far less research has focused on the very different experiences of DD among adults with type 1 diabetes: an autoimmune disease that requires much more intensive and intrusive management than type 2. The proposed research addresses two major gaps in the clinical care of adult patients with type 1 diabetes and DD: to develop a practical, reliable and valid measure of DD for adult patients with type 1 diabetes, with empirically defined cut- points for high DD; and to test a unique, pragmatic, theory based program to reduce high DD among at-risk, poorly controlled type 1 adults. Following a brief measurement study to establish reliable cut-points for high DD, we propose a 2-arm clinical trial to test the comparative effectiveness of an adaptation of Problem Solving Therapy, which now includes a comprehensive focus on emotion, behavior and cognition, with a current standard of care - enhanced diabetes education. Primary outcomes are reduced DD and improved glycemic control. The innovative intervention uses a variety of modalities, some based on electronic and social media (e.g., real time web-based group calls, web-based blogging, personal telephone contact) to enhance participation and retention, and reduce patient burden. We will also assess the impact of selective mediating and moderating variables, collect measures of cost, and will link the findings to dissemination and implementation using RE-AIM.